| |
 |
| Paper: |
Event Analysis in KM3NeT Using Machine Learning |
| Volume: |
532, ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXX |
| Page: |
195 |
| Authors: |
Spisso, B.; KM3NeT Collaboration |
| Abstract: |
This contribution demonstrates the general applicability of convolutional neural networks (CNNs)
in the reconstruction and data analysis of neutrino telescopes, using simulated datasets for the
KM3NeT/ARCA detector as training data. For this purpose, a Keras-based framework called OrcaNet has
been used.
In this work, CNNs are employed to accomplish reconstruction as well as classification tasks for
neutrino events in KM3NeT/ARCA, promising complementary information to the very time-consuming
analysis pipeline based on maximum-likelihood methods. Some CNN models will be described,
which have proved to provide good performance in event reconstruction, e.g. for the estimation of
the energy and the direction of the incoming neutrino and event-shape classification. |
|
|
|
|
|
